ARIA: Adaptive Retrieval Intelligence Assistant -- A Multimodal RAG Framework for Domain-Specific Engineering Education

• URL: http://arxiv.org/abs/2604.06179v1
• Date: Wed, 04 Feb 2026 01:08:24 GMT
• Title: ARIA: Adaptive Retrieval Intelligence Assistant -- A Multimodal RAG Framework for Domain-Specific Engineering Education
• Abstract summary: ARIA is a framework for creating intelligent teaching assistants across university-level courses.<n>ARIA correctly answered all 20 relevant course questions while rejecting 58 of 60 non-relevant queries.
• Score: 1.084959821967413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing effective, domain-specific educational support systems is central to advancing AI in education. Although large language models (LLMs) demonstrate remarkable capabilities, they face significant limitations in specialized educational applications, including hallucinations, limited knowledge updates, and lack of domain expertise. Fine-tuning requires complete model retraining, creating substantial computational overhead, while general-purpose LLMs often provide inaccurate responses in specialized contexts due to reliance on generalized training data. To address this, we propose ARIA (Adaptive Retrieval Intelligence Assistant), a Retrieval-Augmented Generation (RAG) framework for creating intelligent teaching assistants across university-level courses. ARIA leverages a multimodal content extraction pipeline combining Docling for structured document analysis, Nougat for mathematical formula recognition, and GPT-4 Vision API for diagram interpretation, with the e5-large-v2 embedding model for high semantic performance and low latency. This enables accurate processing of complex educational materials while maintaining pedagogical consistency through engineered prompts and response controls. We evaluate ARIA using lecture material from Statics and Mechanics of Materials, a sophomore-level civil engineering course at Johns Hopkins University, benchmarking against ChatGPT-5. Results demonstrate 97.5% accuracy in domain-specific question filtering and superior pedagogical performance. ARIA correctly answered all 20 relevant course questions while rejecting 58 of 60 non-relevant queries, achieving 90.9% precision, 100% recall, and 4.89/5.0 average response quality. These findings demonstrate that ARIA's course-agnostic architecture represents a scalable framework for domain-specific educational AI deployment.

Related papers

• Curriculum as Code: An AI-Assisted Architecture for Instructional Design in STEM Education [0.0]
  This paper presents a six-phase AI-assisted instructional design architecture based on the Curriculum as Code paradigm.<n>It integrates Generative AI with tacit and Python to automate the creation of reproducible, visually consistent, and technically precise materials for STEM education.
  arXiv  Detail & Related papers  (2026-08-07T16:09:10Z)
• AcademiClaw: When Students Set Challenges for AI Agents [69.70303995121154]
  AcademiClaw is a benchmark of 80 complex, long-horizon tasks sourced directly from university students' real academic disconnects.<n>We show that even the best of models achieves only a 55% pass rate.<n>We hope that AcademiClaw and its open-sourced data and code can serve as a useful resource for the OpenClaw community.
  arXiv  Detail & Related papers  (2026-05-04T14:40:42Z)
• Domain Adaptation of Large Language Models for Polymer-Composite Additive Manufacturing Using Retrieval-Augmented Generation and Fine-Tuning [0.0]
  General-purpose large language models (LLMs) often struggle to generate reliable responses in specialized engineering domains.<n>This study investigates strategies for adapting a foundation LLM to the additive manufacturing (AM) domain.
  arXiv  Detail & Related papers  (2026-04-02T20:52:34Z)
• ProfBench: Multi-Domain Rubrics requiring Professional Knowledge to Answer and Judge [94.40918390309186]
  evaluating progress in large language models (LLMs) is often constrained by the challenge of verifying responses.<n>We introduce ProfBench: a set of over 7000 response-criterion pairs as evaluated by human-experts.<n>Our findings reveal that ProfBench poses significant challenges even for state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2025-10-21T17:59:44Z)
• Spec-Driven AI for Science: The ARIA Framework for Automated and Reproducible Data Analysis [23.28226188948918]
  ARIA is a spec-driven, human-in-the-loop framework for automated and interpretable data analysis.<n>ARIA integrates six layers, namely Command, Context, Code, Data, Orchestration, and AI Module.<n>ARIA establishes a new paradigm for transparent, collaborative, and reproducible scientific discovery.
  arXiv  Detail & Related papers  (2025-10-13T08:32:43Z)
• AI-University: An LLM-based platform for instructional alignment to scientific classrooms [12.733784667573211]
  We introduce AI University (AI-U), a flexible framework for AI-driven course content delivery.<n>At its core, AI-U fine-tunes a large language model (LLM) with retrieval-augmented generation (RAG) to generate instructor-aligned responses from lecture videos, notes, and textbooks.<n>We present a scalable pipeline to systematically construct training data, fine-tune an open-source LLM with Low-Rank Adaptation (LoRA), and optimize its responses through RAG-based synthesis.
  arXiv  Detail & Related papers  (2025-04-11T01:26:34Z)
• Enabling Collaborative Parametric Knowledge Calibration for Retrieval-Augmented Vision Question Answering [16.14877145354785]
  Vision Question Answering (KB-VQA) systems address complex visual-grounded questions with knowledge retrieved from external knowledge bases.<n>We propose a unified retrieval-augmented VQA framework with collaborative parametric knowledge calibration.<n>Our approach achieves competitive performance against state-of-the-art models, delivering a significant 4.7% improvement in answering accuracy.
  arXiv  Detail & Related papers  (2025-04-05T05:42:12Z)
• Crossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse Domains [92.36624674516553]
  Reinforcement learning with verifiable rewards (RLVR) has demonstrated significant success in enhancing mathematical reasoning and coding performance of large language models (LLMs)<n>We investigate the effectiveness and scalability of RLVR across diverse real-world domains including medicine, chemistry, psychology, economics, and education.<n>We utilize a generative scoring technique that yields soft, model-based reward signals to overcome limitations posed by binary verifications.
  arXiv  Detail & Related papers  (2025-03-31T08:22:49Z)
• Towards Efficient Educational Chatbots: Benchmarking RAG Frameworks [2.362412515574206]
  Large Language Models (LLMs) have proven immensely beneficial in education by capturing vast amounts of literature-based information.<n>We propose a generative AI-powered GATE question-answering framework that leverages LLMs to explain GATE solutions and support students in their exam preparation.
  arXiv  Detail & Related papers  (2025-03-02T08:11:07Z)
• Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants [176.39275404745098]
  We evaluate whether two AI assistants, GPT-3.5 and GPT-4, can adequately answer assessment questions.<n>GPT-4 answers an average of 65.8% of questions correctly, and can even produce the correct answer across at least one prompting strategy for 85.1% of questions.<n>Our results call for revising program-level assessment design in higher education in light of advances in generative AI.
  arXiv  Detail & Related papers  (2024-08-07T12:11:49Z)
• MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains [54.117238759317004]
  Massive Multitask Agent Understanding (MMAU) benchmark features comprehensive offline tasks that eliminate the need for complex environment setups. It evaluates models across five domains, including Tool-use, Directed Acyclic Graph (DAG) QA, Data Science and Machine Learning coding, Contest-level programming and Mathematics. With a total of 20 meticulously designed tasks encompassing over 3K distinct prompts, MMAU provides a comprehensive framework for evaluating the strengths and limitations of LLM agents.
  arXiv  Detail & Related papers  (2024-07-18T00:58:41Z)
• How to Build an Adaptive AI Tutor for Any Course Using Knowledge Graph-Enhanced Retrieval-Augmented Generation (KG-RAG) [5.305156933641317]
  Large Language Models (LLMs) in Intelligent Tutoring Systems (ITS) presents transformative opportunities for personalized education.<n>Current implementations face two critical challenges: maintaining factual accuracy and delivering coherent, context-aware instruction.<n>This paper introduces Knowledge Graph-enhanced Retrieval-Augmented Generation (RAG), a novel framework that integrates structured knowledge representation with context-aware retrieval.
  arXiv  Detail & Related papers  (2023-11-29T15:02:46Z)
• Building Interpretable and Reliable Open Information Retriever for New Domains Overnight [67.03842581848299]
  Information retrieval is a critical component for many down-stream tasks such as open-domain question answering (QA) We propose an information retrieval pipeline that uses entity/event linking model and query decomposition model to focus more accurately on different information units of the query. We show that, while being more interpretable and reliable, our proposed pipeline significantly improves passage coverages and denotation accuracies across five IR and QA benchmarks.
  arXiv  Detail & Related papers  (2023-08-09T07:47:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.